参考tensorflow2.0 C++加载python训练保存的pb模型
经过模型训练及保存，我们得到“OptimalModelDataSet2”文件夹，模型的保存方法(.h5或.pb文件)，参考【Visual Studio Code】c/c++部署tensorflow训练的模型
其中“OptimalModelDataSet2”文件夹保存着训练好的模型数据"saved_model.pb"
要在OptimalModelDataSet2所在的文件夹下执行：$ saved_model_cli show --dir ./OptimalModelDataSet2会得到：The given SavedModel contains the following tag-sets:'serve'使用'serve'，继续执行：$ saved_model_cli show --dir ./OptimalModelDataSet2/ --tag_set serve会得到：The given SavedModel MetaGraphDef contains SignatureDefs with the following keys:SignatureDef key: "__saved_model_init_op"SignatureDef key: "serving_default"使用上述信息，继续：$ saved_model_cli show --dir ./OptimalModelDataSet2/ --tag_set serve --signature_def serving_default得到：The given SavedModel SignatureDef contains the following input(s):inputs['input_1'] tensor_info:dtype: DT_FLOATshape: (-1, 24)name: serving_default_input_1:0The given SavedModel SignatureDef contains the following output(s):outputs['dense_30'] tensor_info:dtype: DT_FLOATshape: (-1, 1)name: StatefulPartitionedCall:0Method name is: tensorflow/serving/predict这里输入和输出的name，要留好，c中调用会用到。
